LLM-Based Clinical Data Structuring for Medical Imaging
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Solution Overview
Problem
Clinical information is often communicated in unstructured ways, leading to incomplete and inconsistent data transfer between medical institutions and departments, which can result in delayed or disrupted treatments and increased patient risk, particularly due to the lack of unified software solutions and vendor-specific data communication protocols.
Innovation Solution
A computer-implemented method using a large language model to transform unstructured clinical information into structured format, enabling automated extraction and processing of patient-specific data for medical imaging decision support, including scan and post-processing workflows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If unstructured clinical information is used for communication, then flexibility in clinical documentation is maintained, but information completeness and consistency deteriorate
Solution Approach 1:
The patent introduces an intermediary system (large language model) that translates between unstructured clinical documentation and structured data formats. The LLM acts as a mediator that preserves the flexibility of natural language documentation while ensuring complete and consistent information extraction into standardized structures, thereby resolving the contradiction between documentation flexibility and information completeness.
Solution Approach 2:
The system changes the parameter of data structure from unstructured to structured format through automated transformation. By applying parameter changes in the data representation format while maintaining the semantic meaning, the system achieves both flexibility in clinical documentation and completeness in information transfer.
2Ease of operation
If unstructured clinical information is used, then ease of documentation is maintained, but data consistency and reliability deteriorate
Solution Approach 1:
The large language model serves as an intermediary that ensures reliable and consistent data transformation. It maintains the ease of documentation in natural language while guaranteeing data consistency through structured output, thereby resolving the contradiction between ease of documentation and data reliability.
Solution Approach 2:
The system incorporates feedback mechanisms where the structured data can be validated and corrected, ensuring consistency while maintaining ease of documentation. The feedback loop allows for verification of extracted information against the original unstructured text, ensuring reliability.
3Adaptability or versatility
If manual information extraction is used, then flexibility in handling diverse data is maintained, but processing time and workload increase
Solution Approach 1:
The patent replaces manual mechanical information extraction processes with an automated large language model system. This substitution maintains the adaptability and versatility of handling diverse clinical data formats while dramatically reducing processing time and workload through automated transformation.
Solution Approach 2:
The system changes the processing method from manual to automated parameter transformation. By using LLM-based automated extraction, the system maintains flexibility in handling diverse data while improving productivity through rapid automated processing.
4Reliability
If vendor-specific communication protocols are used, then system compatibility within specific vendors is improved, but interoperability between different systems deteriorates
Solution Approach 1:
The patent implements a universal data structure that can handle multiple communication protocols and vendor-specific formats. The large language model system provides multi-functionality by translating between different vendor-specific protocols and a unified standard, thereby achieving both system compatibility and interoperability.
Solution Approach 2:
The LLM-based system acts as an intermediary layer that translates between vendor-specific communication protocols and a universal standard. This mediator approach maintains reliability within specific vendor systems while enabling broad interoperability across different healthcare information systems.
Data Source
AI summary
One or more example embodiments relates to a computer-implemented method for providing medical imaging decision support data, the method comprising receiving natural language data, the natural language data comprising patient-specific clinical information; generating structured information by applying a large language model to the natural language data, the structured information comprising the patient-specific clinical information in a structured format; calculating the medical imaging decision support data based on the structured information; and providing the medical imaging decision support data.